READ: Reverse Engineering of Automotive Data Frames
| Autor: | Dario Stabili, Mirco Marchetti |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Reverse engineering
021110 strategic defence & security studies Computer Networks and Communications Computer science Payload business.industry Real-time computing 0211 other engineering and technologies Automotive industry ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology computer.software_genre CAN bus Domain (software engineering) automotive In-vehicle networks reverse engineering Formal specification Key (cryptography) Safety Risk Reliability and Quality business computer |
| Zdroj: | IEEE Transactions on Information Forensics and Security. 14:1083-1097 |
| ISSN: | 1556-6021 1556-6013 |
| DOI: | 10.1109/tifs.2018.2870826 |
| Popis: | Security analytics and forensics applied to in-vehicle networks are growing research areas that gained relevance after recent reports of cyber-attacks against unmodified licensed vehicles. However, the application of security analytics algorithms and tools to the automotive domain is hindered by the lack of public specifications about proprietary data exchanged over in-vehicle networks. Since the controller area network (CAN) bus is the de-facto standard for the interconnection of automotive electronic control units, the lack of public specifications for CAN messages is a key issue. This paper strives to solve this problem by proposing READ: a novel algorithm for the automatic Reverse Engineering of Automotive Data frames. READ has been designed to analyze traffic traces containing unknown CAN bus messages in order to automatically identify and label different types of signals encoded in the payload of their data frames. Experimental results based on CAN traffic gathered from a licensed unmodified vehicle and validated against its complete formal specifications demonstrate that the proposed algorithm can extract and classify more than twice the signals with respect to the previous related work. Moreover, the execution time of signal extraction and classification is reduced by two orders of magnitude. Applications of READ to CAN messages generated by real vehicles demonstrate its usefulness in the analysis of CAN traffic. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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